Wind power day-ahead prediction with cluster analysis of NWP

Lei Dong*, Lijie Wang, Shahnawaz Farhan Khahro, Shuang Gao, Xiaozhong Liao

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

124 Citations (Scopus)

Abstract

The selection of training data for establishing a model directly affects the prediction precision. Wind power has the characteristic of daily similarity. The corresponding meteorological data also has the characteristic of daily similarity. This paper proposes a new model with cluster analysis of the numerical weather prediction information. The similar day with the predicted day is searched as training sample to a generalized regression neural network model. The numerical weather prediction data and actual wind power data from a wind farm are used in this study to test the model. The prediction results show that correct cluster analysis method is a useful tool in day-ahead wind power prediction.

Original languageEnglish
Pages (from-to)1206-1212
Number of pages7
JournalRenewable and Sustainable Energy Reviews
Volume60
DOIs
Publication statusPublished - 1 Jul 2016

Keywords

  • Cluster analysis
  • Daily similarity
  • Modeling
  • Numerical weather prediction
  • Wind power prediction

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